In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state‐of‐the‐art published methods.

Abstract Optical character recognition systems convert printed or handwritten scripts into digital text formats like ASCII or UNICODE. Urdu-like script languages like Urdu, Punjabi and Sindhi are widely spoken languages of the world, especially in Asia. An enormous amount of printed and handwritten text of such languages exist, which needs to be converted into computer-understandable formats for knowledge extraction. In this study, extreme learning machine’s (ELM’s) most recently proposed variant called deep extreme learning machine (DELM)-based optical character recognition (OCR) system is proposed to enhance Urdu-like script language’s character recognition rate. The proposed DELM-based character recognition model is optimizing the OCR process by reducing the overhead of Pre-processing, Segmentation and Feature Extraction Layer. The proposed system evaluations accomplished 98.75% training accuracy with 1.492 × 10−3 RMSE and 98.12% testing accuracy with 1.587 × 10−3 RMSE, with six DELM hidden layers. The results show that the proposed system has attained the foremost recognition rate as compared to any previously proposed Urdu-like script language OCR system. This technique is applicable for machine-printed text and fractionally useful for handwritten text as well. This study will aid in the advancement of more accurate Urdu-like script OCR’s software systems in the future.

As a computationally efficient tool for the machine’s steady–state performance prediction, equivalent circuit model (ECM) of induction machines (IMs) has been an established option in literature. The results and performance predictions obtained from ECM are significantly affected by: (i) the leakage reactances as the function of the geometry of the machine’s rotor and stator slots (ii) the skin, proximity, and slotting effects and (iii) the saturation of the core. Simultaneous consideration of these effects has been ignored in conventional ECM of IMs for simplicity. In this paper, a novel permeance-based ECM is proposed and developed based on the dimensions of the stator and rotor slots to simultaneously incorporate leakage zig–zag, tooth top and overhang reactances into modeling steps. Saturation, slotting, proximity and skin effects are also fully taken into account to improve the accuracy of the modeling and performance prediction compared to the conventional ECM. Finite element analysis is used to verify the accuracy of the proposed ECM when compared to the conventional ECM based on the steady state performance characteristics such as torque, electromagnetic loss, and efficiency predictions.

Induction machines are a popular choice for tractive applications due to inherent cost savings and performance benefits driving industry to search for an optimal rotor bar design. Induction machines suffer from low torque densities due to larger size and increased losses incurred in the rotor bars making these the performance objectives to be improved through optimization. Communicating through objective functions (OFs), a performance model to rapidly evaluate design parameters coupled with genetic algorithm (GA) can be used to produce an optimal rotor bar; however, conventional OF modeling may introduce function bias or complex coefficient calculations leading to dominated objectives, stalling and premature convergence leading to an unoptimized solution. In this paper, the rotor bar of a squirrel cage induction machine (SCIM) is modeled by a permeance based equivalent circuit model (ECM) creating a link between the rotor slot geometry and equivalent circuit parameters. The model considering skin and slotting effect as well as slot, zigzag, tooth top and overhang leakage reactance effects coupled with a multi- objective GA through novel hyperbolic tangent based OFs to optimize the rotor bar geometry. The optimal rotor bar shape proposed offers increased output torque and reduced total machine losses resulting in a higher operating efficiency.